Regionalization of GRACE data in shorelines by ensemble of artificial intelligence methods
文献类型：期刊论文
| 作者 | Nourani, Vahid3,4,5; Paknezhad, Nardin Jabbarian3,4; Mohammadisepasi, Sepideh1; Zhang, Yongqiang2 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2024-06-01 |
| 卷号 | 636页码:18 |
| 关键词 | Groundwater GRACE data Downscaling Clustering Lake Urmia |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2024.131268 |
| 英文摘要 | Groundwater (GW) plays a crucial role in coastal aquifers and arid regions, serving as a lifeline for communities by providing a reliable and resilient water source, making its monitoring essential for sustainable water management. This study aimed at modeling GW via regionalization of the Gravity Recovery and Climate Experiment (GRACE) data based on two methods. The first method directly regionalized the GRACE data for modeling GW via in situ measurements, including the lake level, precipitation, temperature, observed GW and PenmanMonteith-Leuning (PML) evapotranspiration data. The second method included two stages, in the first stage, the GRACE data were downscaled via the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) data which contains satellite based precipitation, temperature, soil moisture, and snow water equivalent data. In the second stage, the downscaled GRACE was bias corrected to provide regionalized data. Artificial intelligence models consist of shallow networks (Feed Forward Neural Network (FFNN), Adaptive neuro fuzzy (ANFIS), Support Vector Machine (SVR)), the ensemble of shallow networks and Long-Short Term Memory (LSTM) deep learning method were employed in the modeling process and the observed GW level data were targeted for the regionalization. The Link CluE clustering ensemble method was implemented to cluster the piezometers of the aquifer to separate different GW patterns in the area. The proposed methodology was examined over the Miandoab plain, one of the sub-basins of the Lake Urmia, located in Northwest Iran. The modeling results demonstrated that the first method could exhibit superior performance with the Nash-Sutcliffe Efficiency (NSE) of up to 17% higher than the second method. Thus, using in situ observed data for downscaling proved to be more accurate than relying on the data based on the satellite imagery. The results indicated that the ensemble of shallow networks could lead to more precise results than using the deep and shallow learning models, individually, where the NSE for the ensemble of shallow networks was up to 50% higher compared to the LSTM model. |
| 资助项目 | Iran National Science Foundation, through Iran -China (INSF-NSFC) joint projects[4021444] |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001238793100001 |
| 出版者 | ELSEVIER |
| 资助机构 | Iran National Science Foundation, through Iran -China (INSF-NSFC) joint projects |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206688]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Nourani, Vahid |
| 作者单位 | 1.LUT Univ, Sch Engn Sci, POB 20, FI-53851 Lappeenranta, Finland 2.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Univ Tabriz, Ctr Excellence Hydroinformat, Tabriz, Iran 4.Univ Tabriz, Fac Civil Engn, Tabriz, Iran 5.Near East Univ, Fac Civil & Environm Engn, Via Mersin 10, Nicosia, Turkiye |
| 推荐引用方式 GB/T 7714 | Nourani, Vahid,Paknezhad, Nardin Jabbarian,Mohammadisepasi, Sepideh,et al. Regionalization of GRACE data in shorelines by ensemble of artificial intelligence methods[J]. JOURNAL OF HYDROLOGY,2024,636:18. |
| APA | Nourani, Vahid,Paknezhad, Nardin Jabbarian,Mohammadisepasi, Sepideh,&Zhang, Yongqiang.(2024).Regionalization of GRACE data in shorelines by ensemble of artificial intelligence methods.JOURNAL OF HYDROLOGY,636,18. |
| MLA | Nourani, Vahid,et al."Regionalization of GRACE data in shorelines by ensemble of artificial intelligence methods".JOURNAL OF HYDROLOGY 636(2024):18. |
入库方式： OAI收割
来源：地理科学与资源研究所
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